Maps a sample of unknown category to a self-organising map (SOM) stored in a object of type SOMnn.
som.nn.visual(codes, data) \code{data.frame} with 2 columns:
\itemize{
\item Index of the winner neuron for each row (index starting at 1).
\item Distance between winner and row.
}
data.frame with codebook vectors.
data.frame with data to be mapped. Columns of x
must have the same names as columns of codes.
The function returns the winner neuron in codes for
each test vector in x.
codes and x are one vector per row and must have
the same number of columns (i.e. dimensions) and the identical column names.
som.nn.visual is the work horse for the k-NN-like classifier and normally used
from predict.